Recent public health crises have shown the
need for readily available information allowing proper management by
decision-makers. One way of obtaining early information is to involve
data providers who already record routine data for their own use.
We describe here the results of a pilot network carried out by the InVS
(Institut national de veille sanitaire) which gathered data available in
real time from hospital emergency departments and register offices.

Emergency departments data were registered from patients’ computerised
medical files. Mortality data were received from the national institute
of statistics (Insee). Data were transmitted automatically on a daily basis.
Influenza data from outbreaks in 2004/05 and 2005/06 were compared with
data from the sentinel network for the same periods. Environmental health
data were compared with meteorological temperatures recorded in Paris between
June and August 2006. A mortality analysis was conducted on a weekly basis.
Correlation between influenza data from emergency departments and data
from Sentiweb (sentinel network) was significant (p<0.001) for both
outbreaks. In 2005 and 2006, the outbreaks were described similarly by
both sources with identification of the start of the outbreaks by both
systems during the same weeks. As for data related to heat, a significant
correlation was observed between some diagnoses and temperature increases.
For both types of phenomena, mortality increased significantly with one
to two weeks lag.
To our knowledge, this is the first time that a study using real time morbidity
and mortality data is conducted. These initial results show how these data
complement each other and how their simultaneous analysis in real time
makes it possible to quickly measure the impact of a phenomenon.

Introduction
The social and political impacts of health events are essential parameters
to take into account in health surveillance [1]. Recent health events
such as the European heat wave of 2003 and widespread outbreaks of chikungunya,
emphasise the need to provide information to health authorities to help
with decision making [2]. One of the possibilities for obtaining early
information is to involve physicians and others relevant data providers
who record routine data for their own use, which can be transmitted automatically
and daily [3,4]. The French national institute for public health surveillance
(Institut de Veille Sanitaire, InVS) initiated a pilot network in July
2004, gathering different sources of data available in real time from
hospital emergency departments, registry offices, emergency general practitioners
(a service known in France as ‘SOS médecins’). This
article presents an evaluation of this surveillance based on emergency
departments and mortality recording from registry offices for influenza
outbreaks (2005 and 2006) and health impact of the 2006 heat wave.

Material and methodDescription of the networkEmergency Departments (ED)
Data were collected directly from patients’ computerised medical
files filled in during medical consultations. Selected hospitals use
appropriate software. Two architectures for gathering data were used.
The first was based on a regional server in Ile-de-France (Paris area)
developed by regional health authorities. This server centralises data
from hospitals in the area, which are then transferred to InVS. The second
data gathering method consists of a direct connection between hospitals
and the central server at InVS.

Mortality recordings
The national institute for statistics (Institut National de la Statistique
et des Études Économiques, Insee) is responsible for
the administrative recording of deaths from all causes in France. For
several years, Insee has managed a system for recording and centralising
daily mortality. Data processing was near real time. Data from 1152
cities were transmitted daily to InVS.

Variables
Items collected included the diagnosis coded according to ICD-10, with
a score of severity ranked from 1 to 5 after medical examination, the
date of admission to hospital, age, sex, post code, and the chief complaint.
For mortality, only data on age, sex, and date and city of death were
available.
Each patient or death corresponded to a single recording, including all
variables.

Data transmission and processing
Data were transmitted encrypted to InVS over the internet using file
transfer protocol (FTP), seven days a week. Computer assisted extraction
and transmission were performed using specific programmes. These data
were then included in a database, using SAS programmes.
For hospitals, each file transmitted to InVS included all patient visits
to the emergency department logged during the previous 24-hour period
(midnight to midnight). Data were sent according to the hospitals between
4 am and 6 am. They were transmitted twice, at day +1 (temporary file)
and day +2. This double sending allowed the files already transmitted
to be supplemented; the second file automatically superseded the first
one.
Mortality data were transmitted daily and the file included deaths recorded
for the last 30 days.
Data analysis
The study covered the period from July 2004 to the end of July 2006.

Hospitals
We analysed data categorised by week, for the Paris area, in relation
to influenza outbreaks (2005 and 2006), measured through emergency departments
(ICD-10:J10 / J11) compared to data from the Réseau Sentinelles
(sentinel network) which is the reference for studying influenza in France
[5]. A correlation coefficient was performed between the two datasets.
We completed a daily analysis of a number of influenza diagnoses done
in emergency departments with the Cusum method developed by the United
States Centers for Disease Control and Prevention (CDC) within the framework
of the EARS? programme (Early Aberration Reporting System) (6). This
allowed us to define the first days of alert for influenza compared to
onsets published by SentiWeb.
To monitor the health impact of hot weather, we defined an indicator
as follows: total number of daily cases of three pathologies linked to
high temperatures (hyperthermia, dehydratation and hyponatraemia). The
study was focused on the Paris area and data were correlated to daily
temperatures measured in Paris from June to August 2006 by Météo
France® (the French meterological office). Results were compared
with the official periods of alert launched by the French Ministry of
Health (MoH).

Mortality
All-causes mortality analyses were conducted on a weekly basis. The analysis
was based on the method of historical means, adapted from the CDC and
used to monitor infectious diseases [7,8]. For each week, the expected
number (historical mean) of deaths corresponded to the mean of 3 weeks
(comparable, previous, and next weeks) for the past 5 years. The ratios
were computed as 1, plus or minus 2(SD/X), (SD=standard deviation and
X=mean of the 15 considered weeks). When the ratio is outside the thresholds,
the elevated (or diminished) portion of the ratio is significant.
An alert was defined as a threshold-crossing by ratio. The EARS® programme
was run on a daily basis for the whole period.

Results Hospitals
Overall, 46 emergency departments participated in the study. Thirty one
were within Paris area and 15 in other regions, including one overseas
territory in the Indian Ocean (Saint Denis-Reunion Island). Over the
monitoring period, 3.2 million visits were recorded with an average of
4024 visits per day including 980 paediatric visits (< 15) (+/- 25.3%),
2668 adult visits (+/- 15.1%), and 377 visits (+/- 16.7%) to people above
75 years. The medical diagnosis was missing from 26% of records, and
the chief complaint from 12% of records. The severity score was missing
in 17% of cases, and data on sex and age were missing in less than 1%.
Fifty four percent of patients were male and 46% female (P<0.001).
Figure 1 shows the relationship between data from emergency departments
and the Réseau Sentinelles. The two curves were similar, with
a coefficient of correlation of 0.94 (P<0.001). The scales were different
but data from both sources followed a similar kinetic. The outbreak started
in week 3 of 2005, followed by a dramatic increase 3 weeks later. Peaks
were reached in week 7 of 2005 and then decreased for 4 weeks. In the
2006 influenza outbreak, although curves were very similar, there were
some differences. The emergency department influenza visit curve was
above the Réseau Sentinelles from week 45 of 2005 to week 5 of
2006. A gap was observed in week 7 of 2006 with Réseau Sentinelles
data and appeared a week later with emergency department data. A peak
was shown by the Réseau Sentinelles in week 9 of 2006 but not
by emergency departments. Subsequently, an abrupt fall was described
by both sources.

For both outbreaks, EARS® programme was run on a daily basis. In
2005, the first alerts were detected on 16 January 2005 (positive for
C1, C2 and C3 methods), which corresponded to week 3, the first week
of the influenza outbreak onset this season (9). During the following
outbreak, alerts were detected on 29 and 30 January and on 1, 2 and 3
February (positive for C2 and C3) which corresponded to week 5, the first
week of the 2006 outbreak [10].

Regarding the health impact of the 2006 heat wave, the indicator
showed three peaks [FIGURE 2]. The first one was on 19 June,
the second on 3 and 4 July. The first two peaks were correlated
with increased temperatures. The third peak lasted longer (starting
18 July and continuing for nearly 10 days) and was on a large
scale. Between 21 and 23 July, the indicator fell by 35.7%, while
temperature rapidly decreased. Coefficients of correlation between
indicator and daily temperatures were significant (0.67 (P<0.001)
for maximal and 0.72 (P<0.001) for minimal). The EARS® analysis
showed one alert in June (11 and 12 June), two in July (1 to
4 and 18 to 20 July) and one in August (17 August). During this
period, the MoH launched two alerts: 1-4 and 17-25 July.

Mortality
Since the beginning of the study more than 560 000 deaths were recorded.
Out of these deaths, 53% were male and 47% were female (P< 0.001),
representing nearly 1000 deaths per day and two thirds of the French
daily mortality. For any given day, 50% of data were recovered within
a period of 3 days, 90% within a period of 7 days and 95% within a
period of 10 days.
At the national level, the mortality exceeded the alarm threshold during
a 7 weeks time interval (week 6 to week 12 in 2005) and week 29 in July
2006 for the entire period. No other threshold-crossing was identified
[FIGURES 3,4].

Discussion
At this point, networks represent nearly 10% of emergency department
visits in France, and around 66% of the daily mortality.
Among various syndromic surveillance systems tested, none was associated
to two matched data sources in real time (emergency department visits,
crude mortality) [11]. Our first results illustrate the sensitivity of
the system for evaluating the health impact of known events or detecting
a public health threat by its health impact [12]. Consequently, each
emergency department or registry office can be used to capture information,
each patient or death being a source of information [13]. For example,
our system contributed to measure the crude mortality during the chikungunya
outbreak in Reunion in 2005, with no effort expended by the data providers
[14]. In 2003. the monitoring and analysis of the impact of the heat
wave was made possible thanks to the efforts of both data providers and
epidemiologists, and the situation could be understood only after several
weeks [15].
Moreover, the processing for data collection in real time frees the data
collection from one of the major difficulties for health surveillance:
the reporting delay, which can distort the true picture [16].
The lack of 26% of key information (medical diagnosis) can be explained
in two ways: some patients leave emergency departments before a diagnosis
is made (discharge without medical staff authorisation), and others,
for whom no diagnosis was established, are kept in hospitals for further
medical examination; and two hospitals consistently failed to fill in
the diagnosis section of the forms provided. A positive trend of this
percentage has been observed compared to July 2004, when around 40% of
this information was missing, Whatever the rate of missing information,
the medical diagnosis coded in ICD-10 is preferably used than the one
based on chief complaint because of its greater reliability.

Similarity between influenza data based on ED and data from
the Réseau Sentinelle on a weekly basis was confirmed
by the EARS® results. For both outbreaks, the first alerts
detected corresponded to the week of the official onset of these
outbreaks.
The correlation between our ’health impact hot weather’ indicator
and temperatures showed that emergency departments are a very relevant
source of information for environmental health impact surveillance. We
identified a period of alert in June whereas the MoH did not. In July,
two alert periods were identified: the first one on the same day as the
MoH did (1 July 2006) and the second one on 18 July i.e. one day after
the MoH. It is more likely that the August alert detected only by EARS® analysis
was an artefact considering that temperatures were very low.

These validations with two different kinds of disease (infectious and
environmental) allow us to use this data to monitor other infectious
diseases and health impacts of environmental conditions. Furthermore,
its non-specific character made it interesting as a routine surveillance
tool, because it detects less common or emerging diseases [17].

As for mortality, each different threshold-crossing detected
corresponded to widely recognised phenomena (2005 influenza outbreak,
2006 heat wave).
Interestingly, no mortality increase appeared to correspond with the
very small influenza outbreak in the winter of 2005/2006, and during
the period monitored, no health threat with potential impact (infectious
or environmental) on mortality was identified [18].
These three facts demonstrate the interest of this mortality surveillance.
With the implementation of this new surveillance system of all-cause
mortality, we have demonstrated the availability of mortality data in
real time and thus that health impacts of events are becoming quantifiable
in real time. Few systems currently use crude mortality data for health
surveillance in real time, which makes our approach original [19, 20].

This is the first experiment of its kind with syndromic surveillance
in France. The usefulness of emergency departments data for surveillance
had previously been validated by other international experiences.
Here, we corroborate those previous findings in the context of
the French healthcare system and also demonstrate the interest
of ongoing surveillance of crude mortality. The complementarity
of the two data sources, emergency departments and registry offices,
is relevant. In the case of influenza and hot weather, we first
observed an effect on morbidity, followed the week after by an
effect on mortality. Progress is now needed to develop national
coverage of the system, so that it can be efficient in all regions.

Acknowledgments: Ms. Amandine Rodrigues from
Insee and all emergency departments are acknowledged for their
contribution.

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